1. Field of the Invention
This invention relates to nuclear medicine, and particularly relates to nuclear imaging. More particularly it relates to SPECT (Single Photon Emission Computed Tomography) studies which are carried out to form tomographic images. In its most immediate sense, the invention relates to a method of iterative reconstruction of SPECT images from acquired SPECT data.
2. Description of the Background Art
In a conventional SPECT study of an organ such as the heart, a radioisotope (Tc-99m, T1-201, for example) is administered to the patient and the radioisotope is taken up by the heart muscles. Then, the patient is placed in a scintillation camera system and one or more scintillation camera detectors are rotated about the long axis of the patient. These detectors interact with gamma radiation emanates from the patient, and the resulting data is used to form three-dimensional images (“SPECT images” or “tomographic images”) of the distribution of the radioisotope within the patient.
Such three dimensional SPECT images can be calculated based on a set of two-dimensional images (“projections” or “projection images”) acquired by the scintillation camera system; this calculation process is known as image reconstruction. Some of the more common algorithms for image reconstructions are simple back-projection, filtered back-projection, Fourier transformation reconstruction, and a group of algorithms collectively called iterative reconstruction (for example, LSIT: Least Squares Iterative Techniques; ART: Algebraic Reconstruction Techniques; SIRT: Simultaneous Iterative Reconstruction Techniques; gradient and conjugate gradient; maximum entropy; ML-EM: Maximum Likelihood Expectation Maximization; and OSEM: Ordered Subset Expectation Maximization).
Iterative reconstruction methods involve solving a set of algebraic equations to reconstruct the image. The general process is as follows:                a. The computer makes an initial “guess” at the form of the reconstructed image and creates an initial “guess” image;        b. The initial “guess” image matrix is re-projected or back-projected along the original projections;        c. The projection of the “guess” image is compared to the real projection data;        d. The counts/pixel in the image matrix are adjusted until the projection agrees with the real projection data;        e. Steps b-d are repeated for each angle and projection; and        f. The process stops when most of the projections of the image are sufficiently close to the values of the original projection data (i.e. when the process has reached “convergence”).        
As a result, the iterative reconstruction methods are very computer- or calculation-intensive. Until recently, the computing power needed to perform this kind of iterative reconstruction was not readily available in the clinic.
On the other hand, iterative reconstruction methods have advantages over other, simple back-projection methods. For example, the filtered back-projection method reconstructs images from direct calculations from collected projections of activity, and assumes no attenuation of activity. The back-projection thus does not correct the images for attenuation. The iterative reconstruction methods, however, have the ability to correct some of the factors that degrade images in filtered back-projection. For example, the iterative reconstruction methods can correct for attenuation of activity, depth dependent blurring and scatter effects. Therefore, it would be advantageous to provide a method for a faster iterative reconstruction or an iterative reconstruction method requiring less computing power.
In a parallel beam modeling, a reconstruction method called a zoned method is known. A reconstruction space is divided into multiple depth zones that are oriented parallel to the detector. Projection operations are performed for all voxels (volume elements) in one depth zone before handling the next depth zone. For each depth zone, the expanded projection data is convolved with a kernel so that the net projection blur is representative of the average spatial resolution for that zone. The order of zone handling is from nearest the detector to the farthest for back-projection (reversed for projection). This ordering permits the beam function to be modeled with successive convolutions. The central limit property of multiple convolutions is used in approximating a Gaussian beam; efficiency is gained through the use of RECT functions, where convolution requires only 2 additions and 1 multiplication operation per subpixel. Additionally, use of the RECT function on a subpixel matrix allows generation of non-RECT approximations of narrow Gaussians at the pixel level. Projection and back-projection computations are confined to the object and projection support regions. Ray-sum attenuation values are stored for each angle, and applied separately from the blurring operation. Only 1 add and 1 multiply are needed per voxel, for projection or back-projection. These methods can accelerate computation speed 8- to 10-fold and “Fast projection and back-projection with modeling of attenuation and depth dependent resolution for iterative SPECT reconstruction,” J. Nucl. Med., 39(5):79P, and U.S. Pat. No. 5,390,225 are incorporated herein by reference in their entirety.